{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:09:11Z","timestamp":1785542951250,"version":"3.56.0"},"reference-count":44,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T00:00:00Z","timestamp":1651449600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T00:00:00Z","timestamp":1651449600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100003803","name":"University of Hong Kong","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003803","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,2]]},"DOI":"10.1109\/infocom48880.2022.9796935","type":"proceedings-article","created":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T21:18:49Z","timestamp":1655759929000},"page":"1739-1748","source":"Crossref","is-referenced-by-count":201,"title":["Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling"],"prefix":"10.1109","author":[{"given":"Bing","family":"Luo","sequence":"first","affiliation":[{"name":"Shenzhen Institute of Artificial Intelligence and Robotics for Society,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenli","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Science and Engineering, The Chinese University of Hong Kong,Shenzhen,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"IBM T. J. Watson Research Center,Yorktown Heights,NY,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianwei","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Science and Engineering, The Chinese University of Hong Kong,Shenzhen,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leandros","family":"Tassiulas","sequence":"additional","affiliation":[{"name":"Yale University,Department of Electrical Engineering and Institute for Network Science,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488679"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3369583.3392686"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3025446"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3042530"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118436"},{"key":"ref35","article-title":"Resourceefficient and convergence-preserving online participant selection in federated learning","author":"jin","year":"2020","journal-title":"Proceedings of the IEEE International Conference on Distributed Computing Systems (ICDCS)"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2019.2944169"},{"key":"ref10","article-title":"Practical secure aggregation for federated learning on user-held data","author":"bonawitz","year":"2016","journal-title":"NeurIPS Workshop on Private Multi-Party Machine Learning"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155372"},{"key":"ref11","first-page":"747","article-title":"BLENDER: Enabling local search with a hybrid differential privacy model","author":"avent","year":"2017","journal-title":"USENIX Security Symposium (USENIX Security)"},{"key":"ref12","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?n?","year":"2016","journal-title":"NeurIPS Workshop on Private Multi-Party Machine Learning"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118423"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118354"},{"key":"ref15","article-title":"On the convergence of local descent methods in federated learning","author":"haddadpour","year":"2019","journal-title":"arXiv preprint arXiv 1910 14192"},{"key":"ref16","article-title":"Scaffold: Stochastic controlled averaging for on-device federated learning","author":"karimireddy","year":"2019","journal-title":"arXiv preprint arXiv 1910 01500"},{"key":"ref17","article-title":"Achieving linear speedup with partial worker participation in non-iid federated learning","author":"yang","year":"2021","journal-title":"arXiv preprint arXiv 2101 11517"},{"key":"ref18","article-title":"On the convergence of fedavg on non-iid data","author":"li","year":"2019","journal-title":"Proceedings of the International Conference on Learning Representation (ICLR)"},{"key":"ref19","article-title":"Federated learning’s blessing: Fedavg has linear speedup","author":"qu","year":"2020","journal-title":"arXiv preprint arXiv 2007 09948"},{"key":"ref28","article-title":"Client selection in federated learning: Convergence analysis and power-of-choice selection strategies","author":"cho","year":"2020","journal-title":"arXiv preprint arXiv 2010 00170"},{"key":"ref4","article-title":"Towards federated learning at scale: System design","author":"bonawitz","year":"2019","journal-title":"Proceedings of Machine Learning and Systems (MLSys)"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3036952"},{"key":"ref3","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics"},{"key":"ref6","article-title":"Federated optimization in heterogeneous networks","author":"li","year":"2020","journal-title":"Proceedings of Machine Learning and Systems (MLSys)"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737464"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref8","first-page":"7184","article-title":"On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization","author":"yu","year":"2019","journal-title":"Proceedings of the International Conference on Machine Learning (ICML)"},{"key":"ref7","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v33i01.33015693","article-title":"Parallel restarted SGD for non-convex optimization with faster convergence and less communication","author":"yu","year":"2019","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref9","article-title":"Adaptive communication strategies to achieve the best error-runtime trade-off in local-update SGD","author":"wang","year":"2019","journal-title":"Proceedings of Machine Learning and Systems (MLSys)"},{"key":"ref1","article-title":"Advances and open problems in federated learning","author":"kairouz","year":"2019","journal-title":"arXiv preprint arXiv 1912 04977"},{"key":"ref20","article-title":"Straggler-resilient federated learning: Leveraging the interplay between statistical accuracy and system heterogeneity","author":"amirhossein","year":"2020","journal-title":"2012 arXiv preprint arXiv"},{"key":"ref22","first-page":"1017","article-title":"Stochastic gradient descent, weighted sampling, and the randomized kaczmarz algorithm","author":"needell","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref21","first-page":"1","article-title":"Stochastic optimization with importance sampling for regularized loss minimization","author":"zhao","year":"2015","journal-title":"Proceedings of the International Conference on Machine Learning (ICML)"},{"key":"ref42","article-title":"Local SGD converges fast and communicates little","author":"stich","year":"2018","journal-title":"Proceedings of the International Conference on Learning Representation (ICLR)"},{"key":"ref24","first-page":"364","article-title":"Adaptive sampling for SGD by exploiting side information","author":"gopal","year":"2016","journal-title":"Proceedings of the International Conference on Machine Learning (ICML)"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488906"},{"key":"ref23","article-title":"Variance reduction in SGD by distributed importance sampling","author":"alain","year":"2015","journal-title":"arXiv preprint arXiv 1511 06841"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref26","article-title":"Federated learning under importance sampling","author":"rizk","year":"2020","journal-title":"2012 arXiv preprint arXiv"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref25","article-title":"Optimal client sampling for federated learning","author":"chen","year":"2020","journal-title":"arXiv preprint arXiv 2010 13978"}],"event":{"name":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications","location":"London, United Kingdom","start":{"date-parts":[[2022,5,2]]},"end":{"date-parts":[[2022,5,5]]}},"container-title":["IEEE INFOCOM 2022 - IEEE Conference on Computer Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9796607\/9796652\/09796935.pdf?arnumber=9796935","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T20:01:32Z","timestamp":1657569692000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9796935\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,2]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/infocom48880.2022.9796935","relation":{},"subject":[],"published":{"date-parts":[[2022,5,2]]}}}